Would adding a Magnificent 7 factor have improved an equity factor model’s performance in the concentrated U.S. equity markets of the last few years? Our analysis shows that the model’s risk-forecast accuracy would not have materially increased.  

Unusually low small-cap valuations have prompted a role reversal between small caps and their larger-cap peers. Since 2021, they have had a lower forward P/E than large caps and, based on P/B, been consistently cheaper than the cheapest larger caps. 

The risk-on market rally continued in July amid several indicators of frothiness as beta and residual volatility posted the highest factor returns in the U.S. by a wide margin and were also strong globally.

Momentum is a well-documented investment strategy used across assets, industries and asset classes, as well as style factors. We present an optimized style-timing strategy that accounts for both past performance and the variance-covariance of styles.

The FAANG acronym has been used to refer to a dominant group of U.S. stocks for nearly a decade. We analyze new joiners as well as companies that may be next in line to take center stage in the coming years, based on the MSCI Peer Similarity Score. 

Stock markets around the world have heated up this summer. The MSCI ACWI Investable Market Index (IMI) has gained 5% in the third quarter, a partial thaw from its chill when it fell 20% through the first half of the year. 

In this article published in the Journal of Financial Data Science, coauthors from MSCI and MIT develop and describe a data-driven system using AI tools to capture market perception and, in turn, group companies into peer clusters.

The energy sector has become the most crowded with investors, largely due to rising valuations and short interest. We evaluate the impact of its shrinking base of companies as well as the potential implications for net-zero-aware investors. 

The U.S. stock market has plunged 15% to start the year, while growth stocks have fallen even farther. Many firms are suffering through historic drawdowns and a resetting of their valuations.

The MSCI Security Crowding Model extends the framework of the MSCI Factor Crowding Model to individual securities. Our primary motivation was to identify crowded stocks, which we assumed would underperform, on average, and be more susceptible to crashes. If the model is working as intended, we also assume uncrowded stocks would outperform. This would be consistent with our factor-crowding research. Because any security-level characteristic could potentially be a factor, we evaluated our...

We observed historically notable crowding across factors, industries and stocks through the first half of 2021. Examining crowding from multiple perspectives and incorporating multiple data elements provides investors a more holistic view. 

It is not etched in stone that relationships between factor exposures and returns must be linear. We found machine-learning algorithms could identify nonlinear relationships and be used to construct a factor showing significant explanatory power. 

Hedge funds held heavy, relatively illiquid short positions in crowded stocks before the January 2021 short squeeze. We created a risk factor that added explanatory power and may provide insights into potential short squeezes and other risks.

How does one identify or quantify a bubble? We propose a framework for assessing the “bubbliness” of stocks and portfolios, rooted in the idea that bubbles are driven by the same forces, and share characteristics with crowded trades.

We extend our research into sentiment measures by introducing several more traditional and alternative datasets, analyzing their historical and recent performance individually, and as an equal-weighted combination. Our analysis indicates sentiment factors increased transparency into sources of risk and return and the potential for positive risk-adjusted returns, compared to traditional factors.

Positive vaccine news on Nov. 9 caused big moves in industry and style factors. Those hit hardest this year jumped, while previous high performers slumped. Did this mark new factors leadership and a long-awaited rotation from momentum to value?

We investigate the extent to which nonlinearities not captured by standard linear models within equity factor risk models are present. Overall, we found linear models created a robust framework to identify relationships between factor exposures and security returns through simple linear factors or transformed (e.g., polynomial) variants.

A better understanding of the relationships between companies has potential value for many financial applications. We examined three — building simple hedging portfolios, min-vol portfolios and optimized hedging portfolios — and found it reduced portfolio volatilities in all cases.

Fear of a coronavirus pandemic and ensuing economic impacts caused sharp drops in global markets after an initially mild response. We look at recent performance from a factor perspective and how quickly factor returns and volatility reverted in past crises.

Technological advances have expanded the application of factors. What was the realm of quantitative investors has become more accessible, bringing greater transparency and potential insight into portfolio characteristics and performance drivers.

The U.S. price momentum factor, which we highlighted for elevated crowding scores and vulnerability to negative performance at the end of June, suffered sizable drawdowns in the first seven trading days of September.

When factors have historically become crowded, they’ve often experienced significant drawdowns in subsequent months. Which factors were relatively crowded at the end of 2018 — and how did they perform in the first half of 2019?

Among a flood of alternative data sources, consumer sentiment based on citations online stood out.

Peer selection plays a central role in many aspects of finance — including valuation, financial and competitive analysis, risk and portfolio modeling and ESG scoring and alpha generation. One common method of peers identification is industry classification, such as the Global Industry Classification Standard (GICS®). Here we ask if it’s possible to identify and measure relationships between companies from a systematic analysis of various types of data, including fundamentals, news and the...

Early 2019 earnings season has already contained a number of high-profile surprises, such as Facebooks's, but how predictable are these surprises, and what happens when earnings surprises return to trend?

October’s market sell-off reflected investors’ concerns with the sustainability of economic growth, the longer-term impact of trade tariffs and rising interest rates. In all, it seemed to be a shift away from pro-cyclical themes. Do risks remain for those areas of the market?

The momentum factor has been on a tear the last year and a half. Is momentum a crowded trade that has started to unwind?

Elon Musk, founder and CEO of Tesla, suggested in a series of tweets that going private could help Tesla avoid the scrutiny of quarterly reporting and pressure from short selling. Do companies targeted by short sellers share common characteristics? Could factor analysis help investors identify stocks that may become short-selling targets?

FAANG stocks (Facebook, Apple, Amazon, Netflix and Google) make up nearly 40% of the NASDAQ 100 index, and smaller but significant weights in many others. Commonly grouped as tech stocks or growth companies, it seems reasonable to assume they share many similar characteristics. However, when examined through the lens of performance-driving factors, their characteristics are far from homogeneous.

Measuring hedge funds’ positioning and potential crowding around stocks is of interest to many investors, given these funds’ reputation for outperformance. We explore the performance of hedge fund positions using MSCI HedgePlatform, which has advantages over U.S. Form 13F filings, including monthly data points, improved timeliness and full visibility of short positions.

With the rise of factor investing, institutional investors increasingly have sought to understand whether their factor exposures are crowded. Current MSCI Barra equity factor risk models are designed to provide insight and detail to help institutional investors understand how a portfolio is positioned and what has driven its risk and return. The MSCI Integrated Factor Crowding Model is designed to complement the Barra model by providing investors with insight into how the rest of the market...

Investors need a clear and consistent way to talk about factors. For more than 40 years, MSCI has defined how investors use factors to analyze risk and return, from individual stocks to entire portfolios. Factors are important drivers of portfolio performance and are well documented in academic research. They are used to quantify how much risk and return is attributable to different countries, sectors and styles.

Factors are important systematic sources of risk and return in equity portfolios. Given the pervasive use of factors via both active and indexed strategies, a standard approach is needed for defining factors and evaluating the factor characteristics of portfolios. We introduce MSCI FaCS, a classification standard and framework for analyzing and reporting of style factors in equity portfolios that is based on the Barra Global Total Market Equity Model for Long-Term Investors. Managers can use...

Although global uncertainties remain high, the CBOE VIX Index — also known as the “fear index,” recently reached its lowest level since 1993. Some observers have questioned whether VIX remains a reliable indicator.